English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Understanding continuous human actions is a non-trivial but important problemin computer vision. Although there exists a large corpus of work in therecognition of action sequences, most approaches suffer from problems relatingto vast variations in motions, action combinations, and scene contexts. In thispaper, we introduce a novel method for semantic segmentation and recognition oflong and complex manipulation action tasks, such as "preparing a breakfast" or"making a sandwich". We represent manipulations with our recently introduced"Semantic Event Chain" (SEC) concept, which captures the underlyingspatiotemporal structure of an action invariant to motion, velocity, and scenecontext. Solely based on the spatiotemporal interactions between manipulatedobjects and hands in the extracted SEC, the framework automatically parsesindividual manipulation streams performed either sequentially or concurrently.Using event chains, our method further extracts basic primitive elements ofeach parsed manipulation. Without requiring any prior object knowledge, theproposed framework can also extract object-like scene entities that exhibit thesame role in semantically similar manipulations. We conduct extensiveexperiments on various recent datasets to validate the robustness of theframework.

Semantic Decomposition and Recognition of Long and Complex Manipulation Action Sequences